Testing for Idiosyncratic Treatment Effect Heterogeneity

50 Pages Posted: 25 Oct 2021 Last revised: 4 Apr 2023

Date Written: October 19, 2021


This paper provides asymptotically valid tests for the null hypothesis of no treatment effect heterogeneity. Importantly, I consider the presence of heterogeneity that is not explained by observed characteristics, or so-called idiosyncratic heterogeneity. When examining this heterogeneity, common statistical tests encounter a nuisance parameter problem in the average treatment effect which renders the asymptotic distribution of the test statistic dependent on that parameter. I propose an asymptotically valid test that circumvents the estimation of that parameter using the empirical characteristic function. A simulation study illustrates not only the test's validity but its higher power in rejecting a false null as compared to current tests. Furthermore, I show the method's usefulness through its application to a microfinance experiment in Bosnia and Herzegovina. In this experiment and for outcomes related to loan take-up and self-employment, the tests suggest that treatment effect heterogeneity does not seem to be completely accounted for by baseline characteristics. For those outcomes, researchers could potentially try to collect more baseline characteristics to inspect the remaining treatment effect heterogeneity, and potentially, improve treatment targeting.

Keywords: heterogeneous treatment effects, unobserved heterogeneity, policy evaluation, RCTs, empirical characteristic function

JEL Classification: C01, C12, C14, C21, C9

Suggested Citation

Ramirez-Cuellar, Jaime, Testing for Idiosyncratic Treatment Effect Heterogeneity (October 19, 2021). Available at SSRN: https://ssrn.com/abstract=3946092 or http://dx.doi.org/10.2139/ssrn.3946092

Jaime Ramirez-Cuellar (Contact Author)

Microsoft ( email )

14820 NE 36th Street
Redmond, WA 98122
United States

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